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Quantum Information Processing

A game-theoretic security framework for quantum cryptography: Performance analysis and application

Songyang Han, Walter O. Krawec, Fei Miao

Published Sep 22, 2020• 0 citations
Publisher
DOI
10.1007/s11128-020-02861-9

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Quantum networks require continuous parameter calibration to maintain optimal performance as hardware drifts and environmental conditions change. This paper presents a game-theoretic framework for adaptive calibration of universal blind quantum computing networks, treating calibration as a strategic three-player game between classical optical sources, quantum servers, and machine learning agents. The approach integrates stabilized Kerr frequency combs with quantum generative adversarial networks (qGANs) to achieve Nash equilibrium solutions that balance security and performance. Comprehensive experimental validation across multiple quantum hardware platforms demonstrates the framework's effectiveness, with detailed performance metrics presented in Section II. The framework addresses critical scalability challenges while providing formal cryptographic security guarantees through min-entropy analysis of quantum random number generators.
The field of rational cryptography considers the design of cryptographic protocols in the presence of rational agents seeking to maximize local utility functions. This departs from the standard secure multiparty computation setting, where players are assumed to be either honest or malicious. We detail the construction of both a two-party and a multiparty game theoretic framework for constructing rational cryptographic protocols. Our framework specifies the utility function assumptions necessary to realize the privacy, correctness, and fairness guarantees for protocols. We demonstrate that our framework correctly models cryptographic protocols, such as rational secret sharing, where existing work considers equilibrium concepts that yield unreasonable equilibria. Similarly, we demonstrate that cryptography may be applied to the game theoretic domain, constructing an auction market not realizable in the original formulation. Additionally, we demonstrate that modeling players as rational agents allows us to design a protocol that destabilizes coalitions. Thus, we establish a mutual benefit from combining the two fields, while demonstrating the applicability of our framework to real-world market environments. We also give an application of game theory to adversarial interactions where cryptography is not necessary. Specifically, we consider adversarial machine learning, where the adversary is rational and reacts to the presence of a data miner. We give a general extension to classification algorithms that returns greater expected utility for the data miner than existing classification methods.